This invention generally relates to hearing aid digital filters, and more particularly, is directed to a hearing aid digital filter with reduced power consumption and dissipation.
Digital Signal Processors (DSP), which are known to be used in hearing aid systems hitherto, have emphasized speed and performance, and have been traditionally of a design which requires significant amounts of power. Significant power consumption in DSPs is basically because of the components and the manner of interconnection thereof.
The present invention is directed to a DSP for a hearing aid where the power consumption is minimized with consequent advantages of economy and size without sacrificing performance.
In a conventional hearing aid, a microphone converts incident sound waves into an analog electrical signal which is then processed to filter out unwanted noise etc., amplified, and coupled to a receiver or speaker which converts the electrical signal back to sound waves. The electrical signal processor may be an analog processor which operates directly upon an analog electrical signal. Alternatively, the analog signal may be converted to a digital signal and processed by a digital signal processor (DSP).
Most hearing aids now-a-days use analog signal processing. Signal processing schemes include frequency-independent linear amplification, frequency-compensated linear amplification (typically boosting the high frequencies), frequency-independent automatic gain control (AGC) systems, and finally signal level dependent, frequency compensated systems. This final class of hearing aids includes signal processing algorithms that the hearing aid community labels 2-channel systems, 3-channel systems, and multi-channel systems. These systems split the audio frequency band up into two or more sections and can control the gain of each section independently from one another. The K-Amps.RTM. circuits from Etymotic Research and the DynamEQ-I.RTM. circuits from Gennum Corporation are examples of traditional 2-channel systems.
The DynamEQ-I.RTM. has two amplifier sections, one processes the low frequencies and the other processes the high frequencies. This, in the traditional sense, is a two-channel system. The K-Amp contains only one amplifier to shape the frequency response as a function of input level. Both of these circuits implement a first order analog filter although in different ways. Therefore, one must be careful when using the term 2-channel system. A description of the complex-frequency transfer function (written in the s-domain) is a more appropriate way to describe analog filters.
The accuracy and repeatability of analog filters depend on the tolerances of physical components (e.g., resistors and capacitors). These components have initial tolerances, which vary with temperature, and can also vary with humidity, voltage, and age. It can be quite a challenge to design an analog filter to meet its intended requirements when operating over a range of environmental conditions.
Digital filters on the other hand are implemented with digital electronics that manipulate numbers. Digital filters are described by algorithms, and are represented mathematically in the z-domain. The repeatability of the digital filter from circuit to circuit depends only on the accuracy of the sampling frequency. This sampling frequency is usually derived from a crystal-controlled oscillator with a typical accuracy of 0.01 percent. The precision of the oscillator frequency is much better than the typical 1%, 5%, and 10% tolerances of resistors and capacitors used in analog filters. The accuracy, distortion, and noise characteristics of the digital filter depend on the precision with which the signal and filter coefficients are represented.
There exist several prior art US patents which relate to hearing aid technology.
U.S. Pat. No. 4,803,732, entitled Hearing Aid Amplification Method and Apparatus, to Dillon, recognizes different audiofrequency bands and handles them independently. The different frequency bands are independently amplified to match the needs and loss pattern of hearing of the user. Prior to amplification, the microphone signal is passed through an optional filter. Thus, signal discrimination for the user is increased.
U.S. Pat. No. 4,791,672 to Nunley et al is directed to a hearing aid which includes a wearable, programmable digital signal processor for processing digital samples of analog signals in real time. A hearing aid program stored in a signal processor is continuously executed, introducing noise suppression.
Prior art is replete with US patents which are, in one way or the other, directed to noise suppression in different degrees, using different arrangements or techniques. Examples of such prior art include US patents: U.S. Pat. No. 5,794,187 to Franklin, et al., U.S. Pat. No. 5,651,071 to Lindemann, et al., U.S. Pat. No. 4,185,168 to Graupe, et al., U.S. Pat. No. 5,306,560 to Arcos, et al., 5,259,033 to Goodings, et al, and U.S. Pat. No. 5,412,735 to Engebretson, et al. Generally, the aforesaid patents are directed to achieving clarity of hearing or enhanced hearing for the user without any reference to the power consumed in the amplifier and associated circuitry.
A general method for describing the action of a digital filter on a digital signal is the Z-transform method.
Z-Transform
A brief discussion of the Z-transform at this juncture is believed to assist in providing a better understanding of the invention.
Let X (k) be a digital signal that is zero for k&lt;0. Its z-transform, denoted by X*(z), is defined to be the function of z: ##EQU1##
Because of the shift-multiplication property which is being aimed for, z will have the same interpretation as does a phasor approach. That is, when z is on the unit circle in the complex z-plane, its angle is interpreted as a frequency variable. But the question is not how is z interpreted, but rather how is z defined. The answer is: z is an independent complex variable. It has much the same status as k, the sample number. A signal is defined to be a function of k, while its z-transform is defined to be a function of z. Thus, z is the domain of the z-transform of a signal. In fact, the z-transform can be looked at as a transformation from the time domain to the frequency domain. The following symbolism may be used for the z-transform: ##EQU2##
The symbol over the arrow indicates the name of the transformation. It is noted at this point that a digital filter can be thought of as a transformation in the same way as the z-transform. For example, if X is the input filter to a filter H. and Y is the output, a relationship can be expressed as: ##EQU3##
Thus, a filter is a transformation that converts one function of k to another function of k, while the z-transform converts one function of k to another function of z.
Z-transforms have certain properties which make it possible to represent moving average filters and other linear time-invariant filters by multiplication, similar to an approach which deals with phasors. For a one-sided digital input signal, the z-transform of the output signal can be obtained by multiplying the z-transform of the input signal by the transfer function H(z).
Z-transforms have a property which enables easy and free movement from the time domain to the frequency domain. Filtering in the time domain can be looked at as corresponding to multiplication in the z-transform domain. A more complete understanding of the above can be had from An Introduction to Discrete Systems by Kenneth Steglitz, Princeton University, John Wiley & Sons, Inc. 1974.
Filter Coefficients:
A digital filter is characterized by a set of real numbers, namely its coefficients. Altering these numbers will alter the characteristics of the filter. To assess the effect of any change in the value of a coefficient on a given filter characteristic, say the frequency response, one may differentiate .vertline.H(e.sup.jwt).vertline. with respect to that coefficient and use of the value of the derivative as an indication of the sensitivity of the frequency response to changes in the particular coefficient. A more detailed analysis of the choice of coefficients on the filter characteristic can be found in chapter 2, Design of Digital Filters, p. 45 of Digital Signal Processing by Abraham Peled and Bede Liu.
A digital filter H(f) may be expressed as a Fourier series: ##EQU4##
analysis of phase shifts of a symmetric Finite Impulse Response (FIR) as influenced by the choice of the filter coefficient h(n) is given in Digital Signal Processing in VLSI by R. Higgins, pp. 182-191, Prentice Hall, Englewood Cliffs, N.J., 1990.
The choice of the filter coefficients h(n) which are the z-transform of H(z) in the frequency domain and the time domain significantly influence the filter characteristics.
A detailed analysis of the design considerations in designing digital filters, especially from the point of view of the choice of multiplier coefficients can be found in the publication IIR Digital Filter Design using Minimum Adder Multiplier Blocks by A. G. Dempster and M. D. Macleod in IEEE Transactions on Circuits and Systems-IT Analog and Digital Signal Processing, vol. 45, no. 6, pp. 761-763, June 1998. The publication describes details of how specific values chosen for the multiplier coefficients influence the performance of the filter. The publication also explains how the coefficient word length (i.e., the number of bits) required for a digital filter is related to the coefficient sensitivities, and states that different filter structures can require widely different word lengths to meet a required filter specification. A reference is made in the publication to the high cost of coefficient multipliers.
General purpose digital signal processors are available. Motorola, Texas Instruments, and Analog Devices are three examples of manufacturers of these devices. The goal of these processors is to be fast and flexible so that a large number of different types of signal processing can be implemented. The penalty to be paid for this speed and flexibility is power and the size of the integrated circuit. Also, these devices have traditionally operated on a +5 volt supply. The trend is towards lower operating voltages and presently there are several devices that operate on +3.3 volts. The power source for a hearing aid is generally a zinc-air battery that provides +1.3 volts. The end-of-life voltage for the zinc-air cells is usually specified as +0.9 volts, +1.0 volts, or +1.1 volts depending on the manufacturer. Hearing aid integrated circuits are typically specified to operate within specifications down to +1.1 volts. To use digital signal processing in a hearing aid, then requires the DSP chip to operate down to at least +1.1 volts, and preferably +1.0 volts. To maximize battery life, the current drain needs to be very low. For example, in order for a 675-cell, rated at 550 mAh to last for 31 days continuously (24 hours per day), the average current drain on the battery must be no greater than 740 .mu.A (0.74 mA). This current includes the current for the microphone, the analog-to-digital converter (ADC), digital signal processing circuitry (DSP), digital-to-analog converter (DAC), audio power amplifier, and receiver (speaker). To conserve battery current, the audio power amplifier may be implemented as a class-D amplifier. Knowles Electronics offers several receivers with built-in class-D amplifiers. A Knowles electret microphone consumes an average current of about 20 .mu.A, while the class-D amplifier and receiver consume an average current of about 550 .mu.A depending on signal level. This leaves about 170 .mu.A of current available for the remainder of the electronics (ADC, DSP, and DAC). Another attempt to conserve power is to eliminate the conversion from digital back to analog, and rather use a digital-to-digital converter (as in the Widex Sensot.RTM. digital hearing aid, for example). This digital-to-digital converter (DDC) drives the receiver in a class-D scheme, but uses the digital words to directly control the duty-cycle of the signal applied to the receiver.
General purpose DSP chips provide an architecture that is flexible. A generic DSP device consists of various functional blocks including accumulators, adder/subtractors, multipliers, registers, data memory, and program memory. These functional blocks are multiplexed to minimize the number of blocks needed. When two numbers need to be multiplied, the numbers are fetched from memory and latched into the input registers of the multiplier. The output of the multiplier contains the result, which is then stored in another register or in memory.